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This survey aims to analyze and unify various methods in human pose estimation across 2D and 3D contexts.

Human Pose Estimation in 2D and 3D: A Survey of Analytical Methods, Benchmarking Frameworks, and Engineering Applications

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This survey aims to analyze and unify various methods in human pose estimation across 2D and 3D contexts.
Comprehensive review of body representations, processing flows, and problem formulations.
Comparison of benchmark results across datasets including COCO, MPII, and Human3.6M.
Analysis of persistent challenges such as occlusion, domain shift, and real-time processing.
Identified trade-offs between accuracy, robustness, and efficiency in existing methods.
Highlighted gaps in annotation costs, depth ambiguity, and multi-person association.
Outlined emerging directions like data-centric learning and generative motion priors.

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Survey reviews human pose estimation techniques, comparing benchmarks and addressing engineering challenges.

Shrestha et al. (2026) studied this question.

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